Abstract: The model training apparatus trains an image conversion model to generate, from an input image representing a scene in a first environment, an output image representing the scene in a second environment. The model training apparatus inputs a training image to the image conversion model to obtain a first feature map and an output image, input the output image to the image conversion model to obtain a second feature map, computes a patch-wise loss using the features corresponding to a positive example patch and a negative example patch extracted from the training image and a positive example patch extracted from the output image, and trains the image conversion model based on the patch-wise loss, which is extracted intensively from the region representing an object of a specific type.

Abstract: A resource allocation device includes: a request receiving unit that receives a request to allocate a state management unit for sharing a state for each group composed of a plurality of user terminals within a group, to any of a plurality of DCs deployed in a distributed cloud environment; and an allocation calculation unit that calculates an allocation cost of allocating the state management unit to each DC by using an allocation index corresponding to a requirement requested by the group, and determines a DC to which the state management unit is allocated, in accordance with the allocation cost.

Abstract: In the object detection device, the plurality of object detection units output a score indicating a probability that a predetermined object exists, for each partial region set with respect to image data inputted. The weight computation unit computes a weight for each of the plurality of object detection units by using weight computation parameters and based on the image data. The weights are used when the scores outputted by the plurality of object detection units are merged. The weight redistribution unit changes the weight for a predetermined object detection unit, among the weights computed by the weight computation unit, to 0 and output the weights. The merging unit merges the scores outputted by the plurality of object detection units for each of the partial regions, by using the weights computed by the weight computation unit and including the weight changed by the weight redistribution unit.

Abstract: A data classification apparatus includes a data transformation unit that generates a feature vector by using classification target data, a classification estimation process observation unit that acquires, from a classification estimation unit that estimates classification of the classification target data and including a plurality of weak classifiers, observation information in a classification process based on the feature vector, and generates a classification estimation process feature vector based on the observation information, and an error determination unit that determines, in accordance with an input of the classification estimation process feature vector generated by the classification estimation process observation unit and a classification result output from the classification estimation unit to which the feature vector is input, whether the classification result is correct.

Abstract: A server string management device (100) execute, a server string generation step S11 of generating a server permutation combination indicating a combination of servers in a predetermined constraint for a load balancing server, a permutation connection graph generation step S12 of generating a permutation connection graph in which the generated server permutation combination is connected as a node, a connection closed path extraction step S13 of extracting an Euler closed path passing through all the edges once with drawing a edge which passes a path connecting each node on the generated permutation connection graph, and a server string extraction step S14 of generating a server string in which servers at predetermined positions corresponding to each node are arranged along the generated Euler closed path.

Abstract: A gas turbine system includes: a compressor configured to compress external air to generate compressed air; a combustor configured to burn the compressed air together with fuel to generate a combustion gas; a turbine configured to be driven by the combustion gas; an exhaust unit configured to guide the combustion gas, which has passed through the turbine, to outside; a shell having a cylindrical shape and extending along an axis line about which the turbine is configured to rotate and being arranged so as to cover the compressor, the combustor, the turbine, and the exhaust unit; and an exit unit configured to guide the combustion gas, which has passed through the turbine, to a discharge port in the surface of the shell.

Abstract: Provided is a gas turbine system that is used for a moving body including a thrust generator configured to generate thrust from electric power and includes: a combustor that burns a compressed air generated by a compressor together with fuel to generate a combustion gas; a turbine driven by the combustion gas generated by the combustor; a generator that is coupled to the turbine to generate electric power by driving of the turbine and supplies electric power to the thrust generator; a turbocharger that guides external air to the compressor; and an exhaust unit that guides a combustion gas that passed through the turbine to the outside, and the turbocharger is driven by a part of a combustion gas guided to the exhaust unit or by external air heated by heat exchange with a part of a combustion gas guided to the exhaust unit.

Abstract: In an object detection device, the plurality of object detection units output a score indicating a probability that a predetermined object exists for each partial region set with respect to inputted image data. The weight computation unit uses weight computation parameters to compute a weight for each of the plurality of object detection units on a basis of the image data and outputs of the plurality of object detection units, the weight being used when the scores outputted by the plurality of object detection units are merged. The merging unit merges the scores outputted by the plurality of object detection units for each partial region according to the weights computed by the weight computation unit. The first loss computation unit computes a difference between a ground truth label of the image data and the score merged by the merging unit as a first loss. Then, the first parameter correction unit corrects the weight computation parameters so as to reduce the first loss.

Abstract: A server string management device (100) execute, a server string generation step S11 of generating a server permutation combination indicating a combination of servers in a predetermined constraint for a load balancing server, a permutation connection graph generation step S12 of generating a permutation connection graph in which the generated server permutation combination is connected as a node, a connection closed path extraction step S13 of extracting an Euler closed path passing through all the edges once with drawing a edge which passes a path connecting each node on the generated permutation connection graph, and a server string extraction step S14 of generating a server string in which servers at predetermined positions corresponding to each node are arranged along the generated Euler closed path.

Abstract: Provided is a gas turbine system that is used for a moving body including a thrust generator configured to generate thrust from electric power and includes: a compressor that compresses external air to generate compressed air; a combustor that burns the compressed air generated by the compressor together with fuel to generate a combustion gas; a turbine driven by the combustion gas generated by the combustor; a first generator that is coupled to the turbine to generate electric power by driving of the turbine and supplies electric power to the thrust generator; and a second generator that is arranged downstream of the turbine in a flow direction of a combustion gas and converts kinetic energy and/or thermal energy of a combustion gas that passed through the turbine into electric power.

Abstract: An image-processing device includes: a character string detection unit configured to detect a character string of a specific item in a first document image based on a feature amount of the displayed first document image among feature amounts which are recorded in advance based on a result of learning using a plurality of document images and indicate features of the character string of the item for each kind of document image and each specific item; and an output unit configured to output information regarding a correspondence relation indicating the same specific item between the first document image and a second document image displayed to correspond to the first document image.

Abstract: A data classification apparatus includes a data transformation unit that generates a feature vector by using classification target data, a classification estimation process observation unit that acquires, from a classification estimation unit that estimates classification of the classification target data and including a plurality of weak classifiers, observation information in a classification process based on the feature vector, and generates a classification estimation process feature vector based on the observation information, and an error determination unit that determines, in accordance with an input of the classification estimation process feature vector generated by the classification estimation process observation unit and a classification result output from the classification estimation unit to which the feature vector is input, whether the classification result is correct.

Abstract: The present invention discloses a technique for extending threat information and/or generating new threat information by analyzing packet headers flowing through a network using threat information obtained by analyzing malware behavior or the like.

Abstract: The information processing device performs distillation learning of a student model using unknown data which a teacher model has not learned. The label distribution determination unit outputs an arbitrary label for the unknown data. The data generation unit outputs new generated data using an arbitrary label and unknown data as inputs. The distillation learning part performs distillation learning of the student model using the teacher model and using the generated data as an input.

Abstract: An image-processing device includes: a reliability calculation unit configured to calculate reliability of a character recognition result for a document image which is a character recognition target on the basis of a descriptive feature amount of a character string of a specific item included in the document image; and an image output unit configured to output an image of the character recognition result indicating the character string of the specific item in a display mode in accordance with the reliability.

Abstract: An image processing device includes: an identifying unit that identifies a plurality of character strings that are candidates for a recording character string among a plurality of character strings acquired by recognizing characters included in a document image; an output unit that outputs a checking screen that represents positions of the plurality of character strings; and a feature quantity extracting unit that extracts a feature quantity of a character string corresponding to a position identified by a user on the checking screen as a feature quantity of the recording character string.

Abstract: In an object detection device, a plurality of object detection units output a score indicating probability that a predetermined object exists, for each partial region set to image data inputted. The weight computation unit computes weights for merging the scores outputted by the plurality of object detection units, using weight calculation parameters, based on the image data. The merging unit merges the scores outputted by the plurality of object detection units, for each partial region, with the weights computed by the weight computation unit. The target model object detection unit configured to output a score indicating probability that the predetermined object exists, for each partial region set to the image data. The first loss computation unit computes a first loss indicating a difference of the score of the target model object detection unit from a ground truth label of the image data and the score merged by the merging unit.

Abstract: To improve pose estimation accuracy, a pose estimation apparatus according to the present invention extracts a person area from an image, and generates person area image information, based on an image of the extracted person area. The pose estimation apparatus according to the present invention further extracts a joint point of a person from the image, and generates joint point information, based on the extracted joint point. Then, the pose estimation apparatus according to the present invention generates feature value information, based on both of the person area image information and the joint point information. Then, the pose estimation apparatus according to the present invention estimates a pose of a person included in the image, based on an estimation model in which the feature value information is an input, and pose estimation result is an output.

Abstract: In an object detection device, a plurality of object detection units output a score indicating the probability that a predetermined object exists for each partial region set with respect to inputted image data. On the basis of the image data, a weight computation unit uses weight computation parameters to compute weights for each of the plurality of object detection units, the weights being used when the scores outputted by the plurality of object detection units are merged. A merging unit merges the scores outputted by the plurality of object detection units for each partial region according to the weights computed by the weight computation unit. A loss computation unit computes a difference between a ground truth label of the image data and the scores merged by the merging unit as a loss. Then, a parameter correction unit corrects the weight computation parameters so as to reduce the computed loss.

Abstract: Provided is a gas turbine system that is used for a moving body including a thrust generator configured to generate thrust from electric power and includes: a compressor that compresses external air to generate compressed air; a combustor that burns the compressed air generated by the compressor together with fuel to generate a combustion gas; a turbine driven by the combustion gas generated by the combustor; a first generator that is coupled to the turbine to generate electric power by driving of the turbine and supplies electric power to the thrust generator; and a second generator that is arranged downstream of the turbine in a flow direction of a combustion gas and converts kinetic energy and/or thermal energy of a combustion gas that passed through the turbine into electric power.